{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JWB2JF35S7YXIN57OLHX7RZNBI","short_pith_number":"pith:JWB2JF35","schema_version":"1.0","canonical_sha256":"4d83a4977d97f17437bf72cf7fc72d0a336ae866bd6e83a1f500d521ef98b975","source":{"kind":"arxiv","id":"2501.08850","version":1},"attestation_state":"computed","paper":{"title":"Graph Counterfactual Explainable AI via Latent Space Traversal","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Aasa Feragen, Andreas Abildtrup Hansen, Anna Calissano, Paraskevas Pegios","submitted_at":"2025-01-15T15:04:10Z","abstract_excerpt":"Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. Counterfactual explanations aim to explain predictions by finding the ''nearest'' in-distribution alternative input whose prediction changes in a pre-specified way. However, it remains an open question how to define this nearest alternative input, whose solution depends on both the domain (e.g. images, graphs, tabular data, etc.) and the specific application considered. For graphs, this problem is complicated i"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.08850","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-15T15:04:10Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"0a93403246f226493c86970437ac5edfbfcb7aafb993956f29f1df5c8e3cf025","abstract_canon_sha256":"d7501f8f8d0618ac8acba5b7d3858dbf5cfb5936883602fa5374bc95410ad466"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:01:27.037744Z","signature_b64":"AxEFZuQgcF2aZ29IEQuRGAKbvyPKVn803zcyPLIOrtDJAfNliQCMJGfwKLumXkRjW8v+FewOEV9tZ0RZqTvlBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d83a4977d97f17437bf72cf7fc72d0a336ae866bd6e83a1f500d521ef98b975","last_reissued_at":"2026-07-05T10:01:27.037264Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:01:27.037264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Counterfactual Explainable AI via Latent Space Traversal","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Aasa Feragen, Andreas Abildtrup Hansen, Anna Calissano, Paraskevas Pegios","submitted_at":"2025-01-15T15:04:10Z","abstract_excerpt":"Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. Counterfactual explanations aim to explain predictions by finding the ''nearest'' in-distribution alternative input whose prediction changes in a pre-specified way. However, it remains an open question how to define this nearest alternative input, whose solution depends on both the domain (e.g. images, graphs, tabular data, etc.) and the specific application considered. For graphs, this problem is complicated i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.08850","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.08850/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.08850","created_at":"2026-07-05T10:01:27.037327+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.08850v1","created_at":"2026-07-05T10:01:27.037327+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.08850","created_at":"2026-07-05T10:01:27.037327+00:00"},{"alias_kind":"pith_short_12","alias_value":"JWB2JF35S7YX","created_at":"2026-07-05T10:01:27.037327+00:00"},{"alias_kind":"pith_short_16","alias_value":"JWB2JF35S7YXIN57","created_at":"2026-07-05T10:01:27.037327+00:00"},{"alias_kind":"pith_short_8","alias_value":"JWB2JF35","created_at":"2026-07-05T10:01:27.037327+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JWB2JF35S7YXIN57OLHX7RZNBI","json":"https://pith.science/pith/JWB2JF35S7YXIN57OLHX7RZNBI.json","graph_json":"https://pith.science/api/pith-number/JWB2JF35S7YXIN57OLHX7RZNBI/graph.json","events_json":"https://pith.science/api/pith-number/JWB2JF35S7YXIN57OLHX7RZNBI/events.json","paper":"https://pith.science/paper/JWB2JF35"},"agent_actions":{"view_html":"https://pith.science/pith/JWB2JF35S7YXIN57OLHX7RZNBI","download_json":"https://pith.science/pith/JWB2JF35S7YXIN57OLHX7RZNBI.json","view_paper":"https://pith.science/paper/JWB2JF35","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.08850&json=true","fetch_graph":"https://pith.science/api/pith-number/JWB2JF35S7YXIN57OLHX7RZNBI/graph.json","fetch_events":"https://pith.science/api/pith-number/JWB2JF35S7YXIN57OLHX7RZNBI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JWB2JF35S7YXIN57OLHX7RZNBI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JWB2JF35S7YXIN57OLHX7RZNBI/action/storage_attestation","attest_author":"https://pith.science/pith/JWB2JF35S7YXIN57OLHX7RZNBI/action/author_attestation","sign_citation":"https://pith.science/pith/JWB2JF35S7YXIN57OLHX7RZNBI/action/citation_signature","submit_replication":"https://pith.science/pith/JWB2JF35S7YXIN57OLHX7RZNBI/action/replication_record"}},"created_at":"2026-07-05T10:01:27.037327+00:00","updated_at":"2026-07-05T10:01:27.037327+00:00"}